The objective of this document is to: - visually explore the trait database - understand trait coverage - identify data gaps - check for possible inconsistencies
Description of trait databases
Code
# summary of NAsnum_na <-apply(is.na(traits), 2, sum)db_na <- num_na[grep("original", names(num_na))]names(db_na) <-gsub("original_taxa_", "", names(db_na))# database with best matchpar(mar =c(4, 8, 1, 1))barplot(sort(db_na),horiz =TRUE,las =1,xlab ="Number of missing species")
Code
# summary of NAsreal_traits <-!grepl("original", names(traits)) &names(traits) !="accepted_taxa"trait_na <-apply(is.na(traits[, real_traits]), 2, sum)# database with best matchpar(mar =c(4, 8, 1, 1))barplot(sort(trait_na),horiz =TRUE,las =1,xlab ="Number of missing species")
Code
# species with lowest completnesssp_na <-apply(is.na(traits[, real_traits]), 1, sum)names(sp_na) <- traits$accepted_taxaboxplot(sp_na, ylab ="Number of missing traits")
Code
hist(sp_na, xlab ="Number of missing traits", ylab ="Number of species")
Warning in par(usr): argument 1 does not name a graphical parameter
Warning in par(usr): argument 1 does not name a graphical parameter
Warning in par(usr): argument 1 does not name a graphical parameter
Plant height
There are four sources of information for plant height.
Code
# names(traits)[grep("Height", names(traits))]# all in mm2/mgheight <-data.frame("spvignes"= traits$Plant_Height_SPVignes,"lososova"= traits$Plant.height_m_Lososova_2023,"BIEN"= traits$whole.plant.height.m_BIEN,"GIFT"= traits$Plant.height.mean.m_GIFT)pairs(height, lower.panel = panel.smooth, upper.panel = panel.cor)
Warning in par(usr): argument 1 does not name a graphical parameter
Warning in par(usr): argument 1 does not name a graphical parameter
Warning in par(usr): argument 1 does not name a graphical parameter
Warning in par(usr): argument 1 does not name a graphical parameter
Warning in par(usr): argument 1 does not name a graphical parameter
Warning in par(usr): argument 1 does not name a graphical parameter